Ali Muhammad Asif, Caetano-Anollés Gustavo
Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Biology (Basel). 2024 Feb 21;13(3):134. doi: 10.3390/biology13030134.
The slow experimental acquisition of high-quality atomic structures of the rapidly changing proteins of the COVID-19 virus challenges vaccine and therapeutic drug development efforts. Fortunately, deep learning tools such as AlphaFold2 can quickly generate reliable models of atomic structure at experimental resolution. Current modeling studies have focused solely on definitions of mutant constellations of Variants of Concern (VOCs), leaving out the impact of haplotypes on protein structure. Here, we conduct a thorough comparative structural analysis of S-proteins belonging to major VOCs and corresponding latitude-delimited haplotypes that affect viral seasonal behavior. Our approach identified molecular regions of importance as well as patterns of structural recruitment. The S1 subunit hosted the majority of structural changes, especially those involving the N-terminal domain (NTD) and the receptor-binding domain (RBD). In particular, structural changes in the NTD were much greater than just translations in three-dimensional space, altering the sub-structures to greater extents. We also revealed a notable pattern of structural recruitment with the early VOCs Alpha and Delta behaving antagonistically by suppressing regions of structural change introduced by their corresponding haplotypes, and the current VOC Omicron behaving synergistically by amplifying or collecting structural change. Remarkably, haplotypes altering the galectin-like structure of the NTD were major contributors to seasonal behavior, supporting its putative environmental-sensing role. Our results provide an extensive view of the evolutionary landscape of the S-protein across the COVID-19 pandemic. This view will help predict important regions of structural change in future variants and haplotypes for more efficient vaccine and drug development.
新冠病毒快速变化的蛋白质高质量原子结构的实验获取过程缓慢，这给疫苗和治疗药物的研发工作带来了挑战。幸运的是，像AlphaFold2这样的深度学习工具能够以实验分辨率快速生成可靠的原子结构模型。目前的建模研究仅专注于关注变异株（VOCs）突变组合的定义，而忽略了单倍型对蛋白质结构的影响。在此，我们对属于主要VOCs的S蛋白以及影响病毒季节性行为的相应纬度限定单倍型进行了全面的比较结构分析。我们的方法确定了重要的分子区域以及结构募集模式。S1亚基承载了大部分结构变化，特别是那些涉及N端结构域（NTD）和受体结合结构域（RBD）的变化。特别是，NTD的结构变化远不止三维空间中的平移，在更大程度上改变了亚结构。我们还揭示了一种显著的结构募集模式，早期的VOCs Alpha和Delta通过抑制其相应单倍型引入的结构变化区域而表现出拮抗作用，而当前的VOC Omicron则通过放大或汇集结构变化而表现出协同作用。值得注意的是，改变NTD半乳糖凝集素样结构的单倍型是季节性行为的主要贡献者，支持了其假定的环境感知作用。我们的结果提供了新冠疫情期间S蛋白进化景观的广泛视图。这一视图将有助于预测未来变异株和单倍型中结构变化的重要区域，以更有效地开展疫苗和药物研发。
